IIoT stands for Industrial Internet of Things, a term for connected devices in manufacturing, energy, and other industrial practices. IIoT is significant for bringing more automation and self-monitoring to industrial machines, helping improve efficiency.
IIoT devices are often deployed in connection with edge computing.
Edge computing refers to a strategy of shifting computing resources nearer to the physical location of either the user or the source of the data. By placing computing services closer to these locations, users benefit from faster, more reliable services while companies benefit from the flexibility of hybrid cloud computing. Edge computing is one way that a company can use and distribute a common pool of resources across a large number of locations.
It’s common for IIoT devices to be used for edge computing. For example, in a factory setting, machines that gather data for the purpose of real-time data analytics on site would represent an IIoT use case that supports an edge computing strategy.
Achieving these benefits requires an underlying platform that can unify disparate data systems—especially because manufacturing systems traditionally have been isolated from each other.
Under a unified system, manufacturing sites can deploy artificial intelligence and machine learning (AI/ML) model training through a scalable service platform.
The combination of IIoT and edge computing is helping manufacturers solve problems faster by transforming operations, assisting end users in making business decisions, and making plants even more productive.
The IoT, or Internet of Things, is a general term for everyday objects which connect to a network, sending and receiving data to and from other devices. The IIoT is a subsection of the IoT.
Generally, the IoT is made up of any kind of equipment that takes advantage of Internet connectivity in order to send data and receive data. When that equipment is used for industrial purposes, it is considered IIoT.
Consumers IoT devices include products such as connected home thermostats, lights, and door locks. Industrial IoT devices span a wide range of items—everything from water meters to factory machines to sensors on pipelines.
IIoT solutions have a wide variety of use cases, with manufacturing and energy being two of the primary industries to use IIoT.
In manufacturing, providing a view of factory conditions is a common example. Sensor data from machinery, analyzed in real time and fed back to control systems, can lead to improved levels of operational and business efficiency.
In energy, companies can use IIoT to better monitor their field assets. IIoT devices can gather real-time data on electrical grid performance, pipeline flow, or emissions monitoring, even with assets distributed across wide geographic areas.
For example, a water and sewage utility service in Italy uses connected self-service water kiosks across a region to gather real-time data on water quality.
IIoT and automation are tightly linked. Data gathered by IIoT devices can prompt automated tasks that improve efficiency, such as predictive maintenance. Additionally, automation tools can be used to more effectively manage large numbers of IIoT devices.
Using the example of factory machinery, a machine can be programmed to respond to data from onboard sensors—such as noting an increase in vibrations—and automatically take an action—such as alerting an operator that maintenance is required. In ways such as this, IIoT-driven automation can minimize downtime and reduce overall maintenance costs.
Automation can also help with the challenge of managing a large number of IIoT devices, especially ones scattered across large geographic areas—at the edge. Just as automation software can manage servers and network devices, it can also be used to keep IIoT devices updated and validated.
It’s common to hear IIoT systems discussed in connection with Industry 4.0 and the Fourth Industrial Revolution. Both terms mean the same thing, and refer to the increase in automation, communication, and self-monitoring in traditional manufacturing and industrial practices.
Wikipedia traces the phrases “Industrie 4.0” and “Fourth Industrial Revolution” to strategy projects conducted on behalf of the German government in 2011 and 2013. The term “Fourth Industrial Revolution” was further popularized in an article, and later a book, by economist Klaus Schwab.
The three prior industrial revolutions describe the rise of machine manufacturing using steam and water power; the acceleration of industry through railroads, telegraphs, and electricity; and the digital revolution which introduced advanced computing technology.
IIoT overlaps with many of the concepts which are integral to Industry 4.0. These ideas include interconnectivity, information transparency, technical assistance to humans, and decentralized decision-making. The concepts of automation, AI/ML, machine-to-machine communications, and big data are also closely linked to Industry 4.0 and can interlock with the IIoT ecosystem.
A digital twin is a virtual replica of a real-world system, often used to monitor performance and diagnose problems. A digital twin is typically a model of a specific item such as a generator, factory machine, or spacecraft.
Connecting an IIoT device to its digital twin opens opportunities to check a device’s status and detect issues. With this emphasis on connectivity, IIoT and a digital twin strategy can help operators monitor devices, optimize performance, and reduce downtime.
The term “smart city” refers to a concept for managing an urban area using connected technology and data analysis.
Smart cities gather data from devices such as sensors and cameras to support public services including traffic congestion management, crime prevention, and maintenance of assets and facilities.
From traffic sensors to utility meters, the devices commonly used in a smart city are considered part of the IIoT. As with any IIoT use case, the smart city concept depends on connectivity and real-time data collection and analysis.